Design of a novel neuro‐adaptive excitation control system for power systems

Abstract This manuscript proposes a robust excitation control strategy for synchronous generators using backstepping theory and an artificial neural network with a radial basis function to improve power system performance during disturbances and parametric uncertainties. The artificial neural network is used to estimate unmeasurable quantities and unknown internal parameters of a recursive backstepping control. Lyapunov theory is used to carry out the stability analysis and to deduce the online adaptation laws of artificial neural network parameters (weights, centres and widths). To validate the performance of this approach, simulations are performed on an IEEE 9 bus multi‐machine power system. Different test results, compared with those of an existing non‐linear adaptive controller, confirm the high robustness of the proposed method against disturbances and uncertainties.